The present invention relates to a method for automatic classification of a collection of patterns which uses the judgments of human experts on a plurality of sample patterns to organize the collection into sets of similar patterns.
More particularly, the present invention relates to a method for the automatic classification of a collection of patterns, such as image patterns, which uses the so-called "Two Domain Theory" of pattern classification.
Pattern classification by computational devices is usually approached in two phases. The first, a so-called "training" phase is the specification by an expert of pattern exemplars representing the classes as a training set. In the subsequent, so-called "classification phase" pattern features extracted from the target pattern population are joined with the features similarly extracted from the specified exemplars. Various difficulties arise with these techniques in both phases. For example, in the training phase, the expert's knowledge must be properly decoded to record accurately the salient features used for exemplar classification: a process of recognized difficulty with many pitfalls. Additionally, in the classification phase, information from the expert must often be encoded as specific programs for identification and matching, thus restricting the applicable domain of the algorithm. Even the most robust of these methods, the Fisher linear discriminant, where neither the features of the exemplar nor the domain features of the target population of images need be exactly specified, suffers from the noise introduced in exemplars when the expert makes judgments on only a few features of a multi-featured pattern.